Gage locations
USGS2 and DS2 have the same coordinates. USGS1 and DS3 have the same
coordinates.
Hydrographs
Notes on gages: So far DS2 and DS3 have not been used. US1 and DS1
are mostly point measurements.
Streamflow differencing between each gage
DS1 minus US1 (uppermost)
Average DS1 minus US1 difference
## [1] 0.03090149
|
Year
|
Avg streamflow difference (m3/s)
|
|
2011
|
0.0232853
|
|
2012
|
0.0138214
|
|
2013
|
0.0334710
|
|
2014
|
0.0497037
|
|
2015
|
0.0665000
|
|
2016
|
NaN
|
|
2017
|
NaN
|
|
2018
|
NaN
|
|
2019
|
NaN
|
|
2020
|
NaN
|
USGS2 minus DS1
Average USGS2 minus DS1 difference
## [1] 0.04210856
|
Year
|
Avg streamflow difference (m3/s)
|
|
2011
|
0.0363444
|
|
2012
|
0.0106020
|
|
2013
|
0.0944612
|
|
2014
|
NaN
|
|
2015
|
NaN
|
|
2016
|
NaN
|
|
2017
|
NaN
|
|
2018
|
NaN
|
|
2019
|
NaN
|
|
2020
|
NaN
|
USGS1 minus USGS2
Average USGS1 minus USGS2 difference
## [1] -0.0001941755
|
Year
|
Avg streamflow difference (m3/s)
|
|
2011
|
-0.0231274
|
|
2012
|
0.0108739
|
|
2013
|
0.0138716
|
|
2014
|
NaN
|
|
2015
|
NaN
|
|
2016
|
NaN
|
|
2017
|
NaN
|
|
2018
|
NaN
|
|
2019
|
NaN
|
|
2020
|
NaN
|
Sum of losses between USGS1 and USGS2
|
Year
|
Month
|
Monthly sum of losses (m3/s)
|
|
2011
|
4
|
-0.3270991
|
|
2011
|
5
|
-1.0456047
|
|
2011
|
6
|
-3.5999679
|
|
2011
|
7
|
0.0700276
|
|
2011
|
8
|
0.6986662
|
|
2011
|
9
|
-0.0434309
|
|
2012
|
4
|
1.9742787
|
|
2012
|
5
|
0.4860510
|
|
2012
|
6
|
0.1782581
|
|
2012
|
8
|
0.2018424
|
|
2012
|
9
|
-0.0854056
|
|
2013
|
5
|
2.4741706
|
|
2013
|
6
|
-0.5456931
|
|
2013
|
7
|
-0.3966977
|
|
2013
|
8
|
-0.5607375
|
Precip
Compare Niwot SNOTEL precip to prism pixel
Original prism and Niwot SNOTEL precip comaprison
Prism shift 1 day and Niwot SNOTEL precip
Prism shift by fraction and Niwot SNOTEL precip
Compare prism solar rad to flux solar rad
Original Prism and Niwot flux tower solar rad
Prism shift by 1 day versus Niwot flux tower solar rad
Prism shift by fraction versus Niwot flux tower solar rad
Daymet (original) versus Niwot flux tower solar rad
Solar rad gap filling
Runoff ratios
prec_runoff <- read_csv('C:/Users/sears/Documents/Repos/fourmile/data/prec_runoff.csv')
ratios_prep <- prec_runoff %>%
mutate(prec_12 = hru5 + hru6,
prec_13 = hru3 + hru11,
prec_10 = hru1 + hru2 + hru8 + hru9 + hru4,
prec_11 = hru7 + hru10,
date = dmy(time)) %>%
dplyr::select(c(date, prec_12, prec_13, prec_10, prec_11)) %>%
mutate_at(c('prec_12', 'prec_13', 'prec_10', 'prec_11'), ~(./1000)) %>%
mutate(prec_12_cmd = prec_12 * (14378900+16275100),
prec_13_cmd = prec_13 * (6850100+4299900),
prec_10_cmd = prec_10 * (4092400+2170400+2379900+1725600+1967700),
prec_11_cmd = prec_11 * (7092000+5834400)) %>%
dplyr::select(-c(prec_12, prec_13, prec_10, prec_11))
obs_q <- read_csv('C:/Users/sears/Documents/Repos/fourmile/data/orun.csv')
obs_q <- obs_q %>%
dplyr::select(-1) %>%
filter(!row_number() %in% c(1:9, 11:15)) %>%
set_names(as.character(slice(., 1))) %>%
slice(-1) %>%
replace(.==-9999, NA) %>%
rename(ds3 = 2,
usgs1 = 3,
ds2 = 4,
usgs2 = 5,
us1 = 6,
ds1 = 7) %>%
mutate(date = mdy(date)) %>%
mutate_if(is.character, as.numeric) %>%
dplyr::select(-c(ds2, ds3)) %>%
mutate(across(where(is.numeric), ~(.*86400))) %>%
filter(!between(date, as.Date("2013-09-10"), as.Date("2014-04-01")))
ratios <- full_join(ratios_prep, obs_q, by = 'date')
summary(ratios)
## date prec_12_cmd prec_13_cmd prec_10_cmd
## Min. :2011-01-01 Min. : 0 Min. : 0 Min. : 0
## 1st Qu.:2013-06-09 1st Qu.: 0 1st Qu.: 0 1st Qu.: 0
## Median :2015-11-16 Median : 1836 Median : 0 Median : 0
## Mean :2015-11-16 Mean : 92963 Mean : 32610 Mean : 91074
## 3rd Qu.:2018-04-24 3rd Qu.: 62572 3rd Qu.: 15649 3rd Qu.: 42265
## Max. :2020-10-01 Max. :5526738 Max. :2546768 Max. :8107303
## NA's :43 NA's :44 NA's :44 NA's :44
## prec_11_cmd usgs1 usgs2 us1
## Min. : 0.0 Min. : 0 Min. : 486.8 Min. : 864
## 1st Qu.: 0.0 1st Qu.: 1186 1st Qu.: 3512.7 1st Qu.: 2938
## Median : 914.3 Median : 3314 Median : 9588.1 Median : 8597
## Mean : 40929.1 Mean : 17481 Mean : 19677.2 Mean : 20489
## 3rd Qu.: 30861.9 3rd Qu.: 14759 3rd Qu.: 24589.9 3rd Qu.: 24192
## Max. :2301362.4 Max. :694818 Max. :221020.5 Max. :293587
## NA's :44 NA's :546 NA's :3081 NA's :3463
## ds1
## Min. : 864
## 1st Qu.: 2419
## Median : 10123
## Mean : 23001
## 3rd Qu.: 29376
## Max. :318125
## NA's :3459
ratios_wy <- ratios %>%
mutate(wy = calcWaterYear(date)) %>%
group_by(wy) %>%
summarize_if(is.numeric, sum, na.rm=TRUE) %>%
slice(-c(11:12)) %>%
mutate(us1_rr = ifelse(is.na(us1),
NA, us1 / prec_12_cmd),
ds1_rr = ifelse(is.na(ds1),
NA, ds1 / prec_13_cmd),
usgs2_rr = ifelse(is.na(usgs2),
NA, usgs2 / prec_10_cmd),
usgs1_rr = ifelse(is.na(usgs1),
NA, usgs1 / prec_11_cmd))
kable(ratios_wy) %>%
kable_styling()
|
wy
|
prec_12_cmd
|
prec_13_cmd
|
prec_10_cmd
|
prec_11_cmd
|
usgs1
|
usgs2
|
us1
|
ds1
|
us1_rr
|
ds1_rr
|
usgs2_rr
|
usgs1_rr
|
|
2011
|
27342604
|
10218925
|
27838457
|
11851929
|
4164660
|
4531636
|
1080197.1
|
1283376.7
|
0.0395060
|
0.1255882
|
0.1627833
|
0.3513909
|
|
2012
|
28454729
|
10745382
|
29542227
|
13705614
|
1598041
|
1428847
|
233263.1
|
274247.5
|
0.0081977
|
0.0255224
|
0.0483662
|
0.1165975
|
|
2013
|
49402678
|
17797008
|
51739581
|
20596886
|
4755085
|
4350351
|
581017.1
|
627806.4
|
0.0117608
|
0.0352760
|
0.0840817
|
0.2308643
|
|
2014
|
35068774
|
12570023
|
34894899
|
14845227
|
6821680
|
0
|
441475.2
|
517910.4
|
0.0125888
|
0.0412020
|
0.0000000
|
0.4595201
|
|
2015
|
42283708
|
15079372
|
41979012
|
18008048
|
13273551
|
0
|
569548.8
|
650160.0
|
0.0134697
|
0.0431159
|
0.0000000
|
0.7370899
|
|
2016
|
30236488
|
10796870
|
29526701
|
14119850
|
5801479
|
0
|
3888.0
|
4579.2
|
0.0001286
|
0.0004241
|
0.0000000
|
0.4108740
|
|
2017
|
33154630
|
10695612
|
29803300
|
14938516
|
5651282
|
0
|
0.0
|
0.0
|
0.0000000
|
0.0000000
|
0.0000000
|
0.3783028
|
|
2018
|
24878789
|
8264304
|
23018301
|
11390395
|
3315117
|
0
|
0.0
|
0.0
|
0.0000000
|
0.0000000
|
0.0000000
|
0.2910449
|
|
2019
|
29482356
|
9207441
|
25848560
|
13050871
|
4278182
|
0
|
0.0
|
0.0
|
0.0000000
|
0.0000000
|
0.0000000
|
0.3278081
|
|
2020
|
30734909
|
10747941
|
30124906
|
13241031
|
3815151
|
0
|
0.0
|
0.0
|
0.0000000
|
0.0000000
|
0.0000000
|
0.2881309
|
ratios_2014 <- ratios %>%
mutate(wy = calcWaterYear(date)) %>%
filter(wy == 2014) %>%
mutate(usgs1_rr = usgs1 / prec_11_cmd)